Data Tracer Study Analysis in Higher Education Using The FP-Growth Algorithm
DOI:
https://doi.org/10.59188/eduvest.v4i12.50106Keywords:
Data Mining, Fp-Growth, Tracer StudyAbstract
Knowing the distribution of alumni from a tertiary institution is very useful as evaluation material and benchmarks for teaching and learning activities in the tertiary institution concerned. XYZ College alumni distribution data by carrying out tracer studies. Tracer study data can be used by tertiary institutions for decision making and as input in curriculum development or other academic support facilities. Data mining is used to extract information on large-scale data. The method used is the fp-growth algorithm which is part of the association rule technique which aims to find and determine a data set that often appears in a data mine. Attribute data used in this study are field of study, GPA, year of admission, year of graduation, gender, waiting period, field of work, salary, position. The purpose of this study is to examine the pattern of alignment between data on alumni work using the fp-growth algorithm. The results of this study are in the form of information on patterns of alignment of the relationship between fields of study and GPA, year of entry, year of graduation, waiting period, field of work, salary, gender, position on alumni work in tracer study data in Higher Education using the fp-growth algorithm which will make it easier for tertiary institutions to gain deeper insight into alumni, as well as gain new knowledge about graduates and can be used to improve the quality of higher education.
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